Universal machine learning framework for defect predictions in zinc blende semiconductors

We develop a framework powered by machine learning (ML) and high-throughput density functional theory (DFT) computations for the prediction and screening of functional impurities in groups IV, III–V, and II–VI zinc blende semiconductors. Elements spanning the length and breadth of the periodic table...

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Detalles Bibliográficos
Autores principales: Mannodi-Kanakkithodi, Arun, Xiang, Xiaofeng, Jacoby, Laura, Biegaj, Robert, Dunham, Scott T., Gamelin, Daniel R., Chan, Maria K.Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9058924/
https://www.ncbi.nlm.nih.gov/pubmed/35510195
http://dx.doi.org/10.1016/j.patter.2022.100450

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9058924/
https://www.ncbi.nlm.nih.gov/pubmed/35510195
http://dx.doi.org/10.1016/j.patter.2022.100450

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